Coronary artery disease (CAD), heart-related problems and arrhythmias are serious conditions. Early arrhythmia recognition and characterization, such as of myocardial ischemia and infarction, are desirable for cardiac rhythm management to reduce cardiac disorders and irregularities. Known systems for waveform morphology and time domain parameter analysis of depolarization and repolarization, such as of a P wave, QRS complex, ST segment, T wave, for example, are used for cardiac arrhythmia monitoring and identification. However, known clinical methods, which are based on analysis of waveform morphologies and time domain parameters are often subjective and time-consuming and require expertise and clinical experience for accurate interpretation and proper cardiac rhythm management. Some known systems apply more sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis, symbolic complexity analysis and nonlinear entropy evaluation, and focus on generating a new pathology index for qualitative cardiac arrhythmia characterization. These known systems fail to provide adequate information on cardiac electrophysiological function/activity interpretation, tissue mapping and arrhythmia localization.
Known systems typically focus on time (amplitude, latency) or frequency (power, spectrum) domain changes and analysis, which fail to accurately capture and characterize small signal changes (usually undetectable in a signal waveform) in a portion (such as P wave, QRS complex, ST segment) of a heart activity representative waveform. Known systems generate false alarms and often fail to identify arrhythmia and accurately characterize ongoing cardiac events or arrhythmias. Known arrhythmia information extraction systems are often unable to qualitatively and quantitatively characterize small signal changes, and predict a pathological trend, especially in early stages of tissue malfunctioning and cardiac disorders. Known systems typically fail to analyze and identify a real time growing trend of cardiac arrhythmias, such as a pathology trend from low risk to medium, and then to high risk (severe and fatal) rhythm. Known clinical methods for cardiac arrhythmia calculation and evaluation may generate inaccurate and unreliable data and results because of unwanted noise and artifacts. Environmental noise and patient movement artifacts, such as electrical interference, can distort the waveform and make it difficult to detect R wave and ST segment elevation accurately, and even result in a false alarm. A system according to invention principles addresses these deficiencies and related problems.